He Reached $1 Million, Dropped Back to $500K, and Didn’t Have to Start Over
From $500 to Pro M in 11 Months
Ramon reached Pro M status in roughly 11 months, moving through each stage in close to the minimum required time. He started the Seed stage with $500 of his own capital, then increased that amount as he progressed through the program. Eventually, his trading was being replicated against a $1 million allocation. For Ramon, the scale became very real when he looked at the difference between the risk in his own account and the corresponding amount being managed on the larger allocation.
Trading Replaced His Programming Salary
Ramon was already a full-time trader before joining the program. His background was in programming, and his decision to leave traditional employment came only after his trading income became comparable to what he was earning from his salary. He also trades algorithmically, but he does not believe automation magically removes the difficulty of the market. A robot still has to operate in the same market as a manual trader. For him, the advantage comes from building and managing good systems, not simply automating bad ones.
Why He Preferred Trading His Own Capital
One reason Ramon was drawn to the program was that he could continue trading his own money instead of paying for a traditional evaluation. There are still objectives and rules, but he describes the structure as giving him more freedom to trade normally while additional capital is allocated as his results improve. Instead of thinking about the process as passing a one-time exam, he views it as gradually earning access to more capital by demonstrating profitability over time.
Dropping from $1 Million Didn’t Mean Starting Over
Ramon eventually dropped from Pro M to Pro 500. In a traditional evaluation-style environment, that kind of setback could mean losing the account and beginning again from zero. In his case, he retained access to $500,000 of allocated capital. Ramon sees that as one of the biggest differences in the structure because the work of the previous year was not erased in a single moment. He now has a path to rebuild toward Pro M rather than repeating the entire journey from the beginning.
He Replaces Strategies Before They Break the Portfolio
Ramon does not rely on one algorithm. He runs multiple strategies in the same account and continuously develops replacements. Each strategy has a statistical drawdown based on 10 to 15 years of historical performance. When one approaches that historical maximum, he may remove it and replace it with a strategy that has been performing better in demo testing. New systems do not go directly into live accounts. They are tested and monitored first, creating a constant pipeline of alternatives when an existing strategy starts deteriorating.
Trading Strategy/ Mindset
Ramon’s Algorithmic Trading Strategy
Ramon trades algorithmically and runs several strategies inside the same account rather than depending on a single system. His background in programming gives him the ability to continually build and test new strategies, but he does not assume automation eliminates trading risk. In his view, both manual and algorithmic traders are still dealing with the same market.
The most interesting part of his process is how he manages strategy deterioration. Ramon tracks a historical maximum drawdown for each system using roughly 10 to 15 years of data. If a live strategy begins approaching that threshold, he considers replacing it rather than simply waiting indefinitely for recovery.
Those replacements are prepared in advance. New strategies first run in demo accounts where Ramon monitors their behavior. If, for example, a live Nasdaq strategy is entering an unacceptable drawdown, he can compare it with newer Nasdaq systems in demo and replace the struggling strategy with one that has been performing better.
His approach is therefore less about finding one perfect algorithm and more about maintaining a portfolio of systems, measuring them statistically, and continuously developing alternatives.